Results 151 to 160 of about 86,959 (305)
, 2019 Random forests are a statistical learning method widely used in many areas of scientific research essentially for its ability to learn complex relationship between input and output variables and also its capacity to handle high-dimensional data. However,
Thiébaut, Rodolphe +2 more
core
Advanced Materials, EarlyView.We introduce a computational workflow that combines quantum chemical calculations and machine learning techniques to predict the catalytic performance of a wide range of catalysts in the nitrogen reduction reaction (NRR). The analysis of the trained models provides insights into the complex structure–activity relationship in experimental catalytic ...
Leonardo Di Ciano +5 more
wiley +1 more source
Improving Random Forests [PDF]
, 2004 Random forests are one of the most successful ensemble methods which exhibits performance on the level of boosting and support vector machines. The method is fast, robust to noise, does not overfit and offers possibilities for explanation and visualization of its output.
openaire +1 more source
Random Forests with Missing Values in the Covariates [PDF]
, 2010 Rieger, Anna +2 more
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Ultra‐High‐Throughput Discovery of Multifunctional Polyphenolic Coatings on Droplet Microarrays
Advanced Materials, EarlyView.An ultra‐high‐throughput (UHT) combinatorial strategy enables the miniaturized synthesis and screening of ≈30 000 polyamine‐polyphenolic (PaPp) coatings using droplet microarrays (DMA). This approach reveals hundreds of previously unknown fluorescent, redox‐active, and antibacterial materials, including multifunctional, cell‐compatible surfaces ...
Vania Tanda Widyaya +11 more
wiley +1 more source
On the asymptotics of random forests
Journal of Multivariate Analysis, 2016 The last decade has witnessed a growing interest in random forest models which are recognized to exhibit good practical performance, especially in high-dimensional settings. On the theoretical side, however, their predictive power remains largely unexplained, thereby creating a gap between theory and practice. The aim of this paper is twofold. Firstly,
openaire +2 more sources
Tuning Random Forests for Interpretability
, 2023 Random Forests are a widely used predictive technique in the modern data analyst’s toolkit. As with other machine learning methods, Random Forests have hyper-parameters that should be tuned for getting the best predictive accuracy and for interpretation.
Bladen, Kelvyn
core
Recent Advances of Slip Sensors for Smart Robotics
Advanced Materials Technologies, EarlyView.This review summarizes recent progress in robotic slip sensors across mechanical, electrical, thermal, optical, magnetic, and acoustic mechanisms, offering a comprehensive reference for the selection of slip sensors in robotic applications. In addition, current challenges and emerging trends are identified to advance the development of robust, adaptive,
Xingyu Zhang +8 more
wiley +1 more source
Random model trees: an effective and scalable regression method
, 2010 We present and investigate ensembles of randomized model trees as a novel regression method. Such ensembles combine the scalability of tree-based methods with predictive performance rivaling the state of the art in numeric prediction.
Pfahringer, Bernhard
core
Advanced Materials Technologies, EarlyView.Aerosol Jet Printing (AJP) has emerged as a versatile additive manufacturing technique for high‐resolution, conformal, and multi‐material printing. This review highlights advances in printable materials, substrate compatibility, post‐processing, characterization, and process innovations, while critically discussing current challenges and future ...
Chandrachur Chatterjee +2 more
wiley +1 more source